Bayesian Statistics for Evaluation Research: An Introduction
Pollard's introduction to the logic and techniques of Bayesian analysis is aimed at evaluation researchers. Although there is increasing interest in the approach among evaluators, most are unaware of what it has to offer. He addresses basic questions such as: What is it? How can it be applied? How is it different from other approaches? What advantages does it offer? Readers are assumed to have familiarity with programme evaluation and a sound knowledge of statistics.
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Overview of Classical Methods
Probability and Its Interpretation
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assessment axioms basis Bayes Bayesian analysis Bayesian approach Bayesian inference Bayesian methods Bayesian statistics beta distribution chapter classical and Bayesian classical approach classical methods classical test consideration considered cost of error decision maker decision problem decision rule decision theory degrees of freedom discussed equation estimation evaluation research example expected utility expressed framework given hypothesis testing independent individual informative prior interest interval investigator involving joint known variance likelihood function marginal distribution noninformative prior normal distribution normal means Novick and Jackson null hypothesis observations obtained p-value PERCENTILE posterior distribution posterior marginal distributions posterior probabilities precision prior and sample prior beliefs prior distribution prior information prior probabilities prob procedures provides random variable refer regression rejected sample statistics sample sufficient statistics sampling distribution Schlaifer significance level simply specified standard deviation subjective probability Table test statistic theorem tion treatment Type I error uncertainty unknown parameter zero